1. Field of the Invention
The present invention relates to proximity detection and notification of nearby mobile terminals in cellular networks.
2. Overview of the Related Art
Proximity detection and notification is an LBS (Location Based Services) application which increases social context-awareness of mobile users.
Proximity detection and notification (also labeled as proximity alert) applications are already known in the art.
In general, a proximity alert is generated for a first mobile terminal user when a second mobile terminal user (for example belonging to a buddy list of the first user) approaches. By being made aware of the second user proximity, the first user may for example send to the buddy an SMS (Short Message Service) or call him/her and, e.g., agree a place to meet.
A. Amir et al., “Buddy tracking—efficient proximity detection among mobile friends”, IEEE Infocom 2004, 7-11 Mar. 2004, Vol. 1, p. 309, distinguishes two computational frameworks for proximity detection and notification. The first model involves a central server which keeps track of user locations and is responsible for computing and sending the alert messages to all pairs of friends (proximity is always regarded as mutual). In the second model, peer-to-peer, every pair of friends is responsible for keeping each other informed about their location, detecting proximity and sending alert messages. The article describes different algorithms (Strips and quadtree) for minimizing the number of location update messages sent over the network in both frameworks.
A. Kupper and G. Treu, “Efficient proximity and separation detection among mobile targets for supporting location-based community services”, SIGMOBILE Mob. Comput. Commun. Rev., 10(3):1-12, 2006, describes different strategies for proximity detection and separation, as part of a position management framework providing different methods for exchanging position fixes between a GPS (Global Positioning System)—capable mobile device and a central server. The goal is to minimize the amount of exchanged messages. The central model and algorithm described by A. Amir et al. are adopted as a reference for comparison.
WO 2007/059241 provides a system for discovering objects of interest (items, individuals, locations, business services) relevant to the user context. The system comprises, but is not limited to, proximity notifications of nearby individuals (friends, with whom the user has an existing relationship, as well as unknown people matching a certain description). The architecture is based on agents, deployed on both the server and the client side. The discovery (i.e. proximity detection) process utilizes a polling mechanism, following a peer-to-peer computational model when using a peer-to-peer radio technology such as Bluetooth, and a central server model when using a location detection system such as GPS.
US 2008/154697 provides technologies for allowing people to detect others with common interest (like-minded people). Mobile devices configured with information about their users (interests) may federate, typically joining and leaving an ad-hoc federation in a transient manner. The approach follows a peer-to-peer computational model, where proximity detection is conditioned by a previous interest match.
US 2008/294724 provides a solution for enhancing community-based physical location awareness, detecting community members and sending notifications when members fall below a proximity threshold. A central server model is described for all kind of networks, even a Bluetooth-enabled WPAN (Wireless Personal Area Network).
US2009/030999 describes a contact proximity notification application, which notifies a user when one of his/her contacts is nearby. The architecture follows the centralized model and comprises a proximity notification server, a location server and a contacts server. Endpoints may send location updates to the location server, or the latter may periodically query the endpoints. Proximity may be determined by the location server or by the proximity notification server.
K. A. Li et al., “People Tones: A System for the Detection and Notification of Buddy Proximity on Mobile Phones”, University of California, San Diego, Jun. 10-13, 2008, describes a set of methods for detecting proximity, reducing sensor noise in GSM (Global System for Mobile communications) readings (inducing false proximity detection), minimizing power consumption, and generating proximity peripheral cues. The computational model involves mobile terminals pushing their GSM cell tower readings to a central server, which detects proximity and sends notifications.